Customizing haptic feedback in live events

ABSTRACT

A method of generating event identifiers includes receiving sensor information from tracked entities. Based on the sensor information for tracked entities, an event can be determined. An event ID can be assigned to the event based on the type of event that was determined. The event ID can be sent to a haptically enabled device, the device outputting a haptic effect determined from the event ID.

FIELD

One embodiment is directed to a haptic output system. More particularly,one embodiment is directed to a haptic output system that can outputhaptics based on sensor information.

BACKGROUND INFORMATION

Audiences generally appreciate immersive entertainment, such as bigscreens, sophisticated sound effects, and so forth. Sensors can be addedto participants in a live event and haptic effects, such as vibratoryhaptic effects, can be provided to viewers based on sensor output.

Conventional systems provide haptic information from sensors based onraw sensor data. For example, if a sensor is struck or otherwiseimpacted, a vibration corresponding to the impact characteristics of thesensor could be provided to a viewer or audience member.

SUMMARY

Embodiments include a method of generating event identifiers, where themethod includes receiving sensor information from tracked entities.Based on the sensor information for tracked entities, an event can bedetermined. An event ID can be assigned to the event based on the typeof event that was determined. The event ID can be sent to ahaptically-enabled device, the device outputting a haptic effectdetermined from the event ID.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a haptically-enabled system in accordancewith one embodiment of the present invention.

FIG. 2 is a block diagram of a haptically-enabled system in accordancewith one embodiment of the present invention.

FIG. 3 is a flow diagram for providing event IDs in accordance with oneembodiment.

FIG. 4 is a flow diagram of providing event IDs in accordance with oneembodiment.

FIG. 5 is a flow diagram of providing haptic output at a haptic outputdevice in accordance with one embodiment.

FIG. 6 illustrates an example merged sensor packet format in accordancewith some embodiments.

FIG. 7 is a diagram of a timeline to illustrate the synchronization ofhaptic event IDs with other broadcast data.

FIG. 8 is a flow diagram for determining an event based on sensorinformation in accordance with one embodiment.

FIG. 9 shows an example output of z-acceleration values that can besampled following a hit.

FIG. 10 is a block diagram of a haptically-enabled system in accordancewith one embodiment.

DETAILED DESCRIPTION

Embodiments allow viewers or an audience of an event, such as a homeviewer of a sporting event, to experience the event more immersively byproviding haptic feedback to the audience member based on actionhappening at the event. For example, haptic feedback provided to anaudience member when a player makes a tackle can give the audiencemember an enhanced viewing experience.

Embodiments described below provide an audience member (i.e., a user)the opportunity to receive haptic feedback related to the happenings atthe event. In addition, embodiments allow the user to providepreferences that indicate a source for generating the feedback. In otherwords, the user can provide preferences that select which events toreceive haptic feedback for based on who is performing the action orwhat action is being performed. The user can select which entity theywant to follow, whether it is a group entity such as a whole team in amatch, or a single entity such as a single player in a sports event. Forexample, a user can receive feedback for events relating to an entity,object, a player, a team, and so forth by specifying the classificationor source of events the user is interested in. In addition, embodimentscan provide sensor tracking of entities, objects, players, persons, andso forth. Based on the sensor output, embodiments can determine an eventcorresponding to the sensor output and assign that event an identifier(“ID”).

Embodiments therefore do not directly translate a sensed action into ahaptic effect. Potential problems with directly converting sensorreadings into haptic effects can include connectivity issues, apotential loss of information, and synchronization issues. Connectivityissues can arise due to a requirement for large transmission bandwidthto provide sensor information from wirelessly connected sensors into ahaptics output engine. This can also lead to a loss of information and adecrease in haptic quality, especially when a lot of sensor data isbeing transmitted at once, because there can be drops in datatransmission. Synchronization issues can also develop between hapticsand video/audio data resulting from transmission latency or otherissues. Determining an event identifier associated with the sensedactions requires much less bandwidth than raw sensor data and can beaccomplished in a live environment.

In some embodiments, the source of the sensor information can beclassified and the classification information can be included asmetadata to the event ID. The metadata can include a classification IDor source ID or an ID type corresponding to the classification orsource. For example, in a sporting event, sensors located on each playercan be identified by a sensor ID and the sensor ID(s) on each player canbe associated together as a player ID. The player ID can be associatedwith the event ID to indicate which players are involved in theparticular event.

Classification can also include hierarchical arrangements. For example,a source classification can include a player ID and a team ID, where theteam ID is determined depending on which team the player is on and wheremultiple players can be associated with the same team ID. In anotherexample, a classification can include “scoring objects” and asub-classification can include a “ball” or “team goal.” In the formerexample, if a player tackles another player in a football game, an eventID for tackling can be created along with a classification ID or sourceID for each team and each player. In the latter example, if the ballcrosses the goal line, an event ID for scoring can be created along withclassification or source ID for the team goal, ball, and player carryingthe ball when the ball crosses the goal line.

Embodiments can send the event ID (and optional classification or sourceID) information to remote users watching through, for example, ahandheld device with a haptic output device or a television whileinteracting with a wearable device that contains a haptic output device.In some embodiments, event IDs (and optional classification or sourceIDs) can be merged with broadcast data as a sub-channeled, multiplexed,or additional stream to the broadcast data. In some embodiments, eventIDs (and optional classification or source IDs) can be merged togetherinto a multi-channel stream that can be sent and received over anyavailable wireless technologies, such as Wi-Fi, Bluetooth, and so forth.

Embodiments can use the event ID (and optional classification or sourceIDs) information to map the event information to a corresponding hapticeffect. For example, an event ID can be an identifier that correspondsto a tackle event with an intensity level of three. The event ID can beused to find haptic effect information to be played on a haptic outputdevice, such as a haptic playback track or other such information tocause the haptic output device to generate a haptic effect as feedbackbased on the event ID.

As used herein, a wearable device is any device that can be worn by auser and that is in contact directly or indirectly, permanently orintermittently, with a user's body part. A graspable (by the hand)device is also considered a wearable device even if it is only“wearable” so long as it is grasped, such as an arm rest of a chair or aphone.

FIG. 1 is a block diagram of a haptically-enabled system 10 inaccordance with one embodiment of the present invention. System 10includes sensors 20 that can be attached to or integrated with entities,objects, persons, players, and the like at an event. Each of the sensorscan include a sensor ID so that sensor information provided by thesensor includes which sensor provided the sensor information. Sensor IDscan be correlated with the name of the entity, player, person, object,and the like. For example, a sensor ID “ABC” can correspond to a sensorplaced on “A. Team Player.” Sensor information received from sensor ID“ABC” can be assigned to “A. Team Player.” Discussed in more detailbelow, the assignment of a sensor ID to a list of entities, objects,persons, players, and the like, allows a classification or source to bedetermined for events that are found from sensor data. When an eventoccurs, sensors can be used to identify the event, and who and/or whatwas involved in the event.

Sensors 20 can include temperature/humidity/atmospheric pressure sensorsto capture environmental conditions, an inertial field measurement unit(“IMU”) with accelerometer, gyroscope, and magnetometer to characterizethe motion, velocity, acceleration and orientation of the entity, amicrophone to capture voice or environmental audio information,bio-sensors (e.g., blood pressure sensors, skin resistance, etc.).Sensors can use wireless transceivers/transmitters (not pictured) toreceive/transmit information from/to other devices wirelessly. One ofskill in the art will understand that sensors can be used individuallyor in any combination.

System 10 includes an event process server 22 that receives sensorinformation and determines an event from the sensor information. Eventprocess server 22 can use sensor information to determine what kind ofevent took place. An event ID can be assigned to the event. For example,sensor information can be analyzed by event process server 22 todetermine that Player 3 from Team A scored a touchdown by a lunge acrossthe goal line. An event ID can be assigned to that event so that, ratherthan transmit information specifying a haptic feedback effect directly,the event ID can be used at the user's end and cause an appropriatehaptic effect to play on the haptic output device. Examples of eventcategories include a ball being hit, scoring, tackling, and so forth.Event IDs can be assigned to any event, such as anything out of theordinary flow that can be advantageous to provide haptic effects for.The transformation from event to event ID can be made by determining anevent category based on the sensor information and then determining thetype of event that is within that category. For example, in a footballgame sensor information can be used to determine that a ball movementevent took place by analyzing information from a sensor placed on thefootball. Sensor information can be further analyzed to determine that ascoring event took place based on sensor information for the ball andsensor information for players in proximity to the ball.

Rather than determining the type of action taking place, event processserver 22 can use sensor data to assign an event ID based on the sensorreadings and sensor context. The event ID can be a transformation of Ninput sensory signals, such as acceleration (x, y, z), gyroscope (x, y,z), magnetometer (x, y, z), etc. into M outputs classifying an event ID.For example, sensor data received from a sensor ID associated with aball can include a sudden change in acceleration and gyroscope sensoroutputs. An event ID can be selected for the ball associated with thesensor ID based on the acceleration and gyroscope parameters. Thus,rather than determining that the ball had been kicked, the data can betranslated more directly to an appropriate event ID while still usingsome context for the sensor, such as the sensor ID or otherclassification or source ID.

Algorithms for determining what kind of event took place can depend onthe venue and type of event. Events associated with football games wouldbe different than events associated with golf or soccer. In someembodiments, sensor ID information can be used to determine theclassification or source of the entity, object, person, player, or thelike whose action or involvement generated the sensor information.Classification or source IDs can be assigned to the event. For example,a player dunking a basketball can be assigned a source ID for theidentification of the player and a source ID for the team the player ison. In some embodiments, “source” can be substituted for the thing thatis classified. For example, the source ID could be “player ID,” “teamID,” “goal ID,” “hole ID,” “object ID,” “sideline ID,” and so forth.

In team sporting events, for example, each team can be identified with ateam ID. A player ID associated with one or more sensors for each playercan be provided as a sub ID to identify all the players within the team.In sports with equipment unique to each player, entity identificationfor a player can be based on the sensor ID for the equipment associatedwith that player. In sports with shared equipment, like soccer, entitysensors on players can identify players and an entity sensor on the ballcan identify actions and events related to the ball (and the playersnear the ball when the event occurs).

In some embodiments, sensor information 20 can be merged together into amultichannel feed prior to being received by the event process server.For example, a sensor interface 21 can receive all available sensorinformation, merge the sensor information together into a multichannelfeed, and send it to the event process server.

As an example of the merging, suppose that all possible IDs in a givenbroadcasting event can be represented for example as: Player ID1, PlayerID2, . . . , Player ID_N, team ID1, team ID2, Object ID1, Object ID2, .. . Object ID_N. Not all sensors objects, players, etc., however,necessarily trigger an event and therefore sensor interface 21 might notreceive all IDs at a given time. Interface 21 will generally onlyreceive the IDs that are “active.” In other words, sensors can beconfigured to not send data until the data meets a certain thresholdvalue for each sensor. From list of all possible IDs, now supposeinterface 21 receives the following IDs: Player ID2, Team ID1, ObjectID1, Object ID2. Sensor interface 21 can merge these IDs into a singlepacket by, for example concatenating the different IDs. One possibleimplementation is illustrated by the following example packet format ofFIG. 6.

FIG. 6 illustrates an example merged sensor packet format in accordancewith some embodiments. Element 610 is a packet header that can containinformation about the packet and communication protocol for the packet,such as overall length, encoding, rate, and the like. Element 620 is afield for indicating the number of IDs being transmitted in the packet.Element 630 is a field for the first ID corresponding to one of theactive IDs in the event. Element 640 is a field for data for the firstID. This data would correspond to data particular to the sensor. The IDsand data repeat until the last data field. Element 650 is a packet tailfield indicating the end of the packet. The packet can be transmittedusing known wireless technologies, such as WiFi, Bluetooth, or mobiledata network technologies.

In the list of active IDs from above, Player ID2, Team ID1, Object ID1,and Object ID2, the first ID 630 would correspond to Player ID2, thesecond ID would correspond to Team ID1, the third ID would correspond toObject ID1, and the fourth ID would correspond to Object ID2. Note thatthis implementation considers how the packets can be created in order totransmit the information from sensor interface 21 to event processorserver 22. One of skill will understand that the packet can be arrangedin any other way suitable for a particular implementation and that thearrangement discussed in relation to FIG. 6 is merely one example.

One purpose of the merging is to reduce the communication time and/orbandwidth between sensor interface 21 and event processor 22. In oneembodiment, sensor interface 21 can capture data 20 at a fast rate,create a bundle of sensor information, and transmit it at a lower rate(e.g., perhaps at a rate at which television information is regularlybroadcast).

Having a sensor interface local to the place where the sensors arelocated can be convenient. With a local sensor interface, transmissionenergy required from each sensor can be reduced. Event process server 22can be located locally or remotely, as desired. Also, in someembodiments, multiple sensors can be grouped together, for example, bysource or classification. For example, in the case where a player hasmultiple entity sensors, information from these sensors can be grouped.

In some embodiments, the functionality of event process server 22 can beprocessed in a distributed manner. For example, a smart tennis racquetcan contain several sensors that are processed by an event processserver just for that racquet. Devices such as sports equipment cancontain sensors that relay sensor information or process sensorinformation and provide event ID information using Bluetooth or anInternet of Things paradigm. For example, smart programmable sensors canbe used that provide a remote interface and the capability to configuresettings for the sensors. Sensors can be equipped with a local processorto handle some processing capabilities. In some embodiments, sensorinterface 21 can serve as a configuration point for managing sensorinformation and provide a remote interface for changing sensor settings.

System 10 can include an event merger 24 that can merge event IDs into amultichannel feed. Each channel in the multichannel feed can correspondto a haptic channel that provides an event ID and classification orsource IDs associated with the event IDs. Each channel can correspond toa particular team, player, or other entity. System 10 can include atransmitter/antenna 26 for sending event IDs. In some embodiments, theevent IDs can be broadcast along with one or more audio and one or morevideo feeds. In some embodiments, such as where data will be streamed,the event IDs can be sent using a computer network, including wired andwireless technologies.

For many live broadcast events, a delay is purposefully added to screenfor inappropriate broadcast content or bloopers. In embodiments wherethe processing of haptic information exceeds real-time processing (orthe processing of video/audio signals), the intentional delay can beused to perform the event processing. The haptic event IDs can besynchronized to the video/audio signals, such as described below.

FIG. 7 is a diagram of a timeline to illustrate the synchronization ofhaptic event IDs with other broadcast data. Timeline 710 is a timelineof the broadcast of a signal for a live event, such as a multimediasignal with one or more video signals and one or more audio signals. Alive event occurs at time 720. Video and audio for the live event isprocessed at time 730. Sensor data, such as data from sensor 20, isprocessed at time 740, including event ID assignment and merging atevent merger 24. At time 750, the information including video, audio,and haptic data can be broadcast. At time 760, the information can bereceived. This progression of time sequences sets up particular timedifferences that are illustrated below timeline 710. Time t1 is the timelapse that occurs between a live event 720 and the time the video andaudio associated with the event is processed and ready to be broadcastat 730. Time t2 can be a deliberate delay, such as a seven second delaythat is typically used in U.S. television broadcasting. Time t3 can bethe time lapse between the live event 720 and the processing of sensordata into haptic event ID information at 740. In this illustration, thisprocessing takes longer than the video/audio processing. Thus the delayof t2 can be used to complete processing. The haptic signal andvideo/audio data can be synced by adding a corresponding delay to thehaptic signal, t4, equal to t1+t2−t3. This same equation can be used insituations where the haptic information is processed quicker than thedelay at t1, or in other words, when t3<t1. Or, where t3<t1, instead adelay equal to t1−t3 can be added to the haptic information signal, andboth signals can be delayed by an optional common delay time, t2. Timet5 is the time lapse due to broadcast, which varies based on thebroadcast methodology and technology. In some embodiments, where nodeliberate delay is added, the video/audio signal can be delayed to syncwith the haptic signal by a delay time equal to t3−t1.

In some embodiments, delay t1 can be assumed to be very close to zero asa result of real-time processing. In some embodiments, delay t2 can beassumed to be the standard seven seconds used in U.S. televisionbroadcasting of live events. In some embodiments, delay t3 can becontrolled by increasing or decreasing the number of reading rounds ofthe sensors for information to be analyzed. For example, when each ofthe reading rounds of sensors is the same duration, i.e., at the samesample rate, then increasing the number of reading rounds of the sensorwill cause an increased delay when processing sensor data into eventIDs.

System 10 can include a second process server 28 for receiving andprocessing event IDs. Process server 28 can separate merged event IDsinto individual event IDs. In some embodiments, process server 28 canfilter event IDs based on output preferences or device capabilities.Process server 28 can send the event IDs or filtered event IDs todevices such as device 30. Filtering based on output preferences caninclude a preference, for example, for event IDs associated with aparticular classification or source. In some embodiments process server28 can use classification or source IDs sent with event IDs to performfiltering based on user preferences. Using output preferences, a usercan enable haptics for a team, player, event category, and so forth. Thepreferences can serve to filter the available event IDs to output hapticeffects that correspond to the user selections in the outputpreferences. If multiple video or audio feeds are available, outputpreferences can also include a selection of a particular video or audiofeed for the live event. Filtering based on device capabilities caninclude selecting event IDs that correspond to a haptic effects intendedto be played on certain haptic output devices.

Devices, such as device 30, can receive one or more event IDs fromprocess server 28 and determine one or more haptic effects associatedwith the event IDs. Device 30 can output the haptic effects as hapticfeedback on a haptic output device, such as a vibrotactile orkinesthetic actuator.

In some embodiments, process server 28 or aspects of process server 28functions described above with respect to process server 28 can beintegrated into device 30. For example, in one embodiment a mergedstream of event IDs can be received by device 30, which can separate thestream into individual event IDs and filter them to select filtered IDsbased on output preferences on device 30.

FIG. 1 depicts a soccer field with a left half 50, a right half 60, anda soccer ball 70 as an example of the possible placement of sensors 20of system 10 onto entities team A players 51, team B players 61, goals62, and ball 70. As described above, sensors 20 can send sensorinformation to event process server 22 as the entities interact.

FIG. 2 is a block diagram of a haptically-enabled system 210 inaccordance with one embodiment of the present invention. System 210 caninclude a handheld or wearable device 30 of FIG. 2 that includes ahaptic output device 218. Internal to system 210 is a haptic feedbacksystem that generates haptics on system 210. In one embodiment, a hapticeffect is generated by haptic output device 218.

The haptic feedback system includes a processor or controller 212.Coupled to processor 212 is a memory 220 and a haptic output devicedrive circuit 216, which is coupled to a haptic output device 218located on handheld or wearable device 30. Haptic output device 218 caninclude any type of haptic output device, including motors, actuators,electrostatic friction (“ESF”) devices, ultrasonic frequency (“USF”)devices, and any other haptic output device that can be used to providehaptic feedback to a user.

Other haptic output devices 218 may include flexible, semi-rigid, orrigid materials, including smart fluidic actuators, rheological fluidicactuators, Macro-Fiber Composite (“MFC”) actuators, Shape Memory Alloy(“SMA”) actuators, piezo actuators, and Micro-Electro-Mechanical System(“MEMS”) actuators.

Processor 212 may be any type of general purpose processor, or could bea processor specifically designed to provide haptic effects, such as anapplication-specific integrated circuit (“ASIC”). Processor 212 may bethe same processor that operates the entire system 210, or may be aseparate processor. Processor 212 can decide what haptic effects are tobe played and the order in which the effects are played based on highlevel parameters. A haptic effect may be considered “dynamic” if itincludes some variation in the generation of haptic effects amongsthaptic output device(s) or a variation in the generation of hapticeffects based on a user's interaction with handheld or wearable device30 or some other aspect of system 210, such as user preferences thatfilter for certain types of event IDs.

Processor 212 outputs the control signals to haptic output device drivecircuit 216, which includes electronic components and circuitry used tosupply haptic output device 218 with the required electrical current andvoltage to cause the desired haptic effects. System 210 may include morethan one haptic output device 218, and each haptic output device mayinclude a separate drive circuit 216, all coupled to a common processor212. Memory device 220 can be any type of storage device orcomputer-readable medium, such as random access memory (“RAM”) orread-only memory (“ROM”). Memory 220 stores instructions executed byprocessor 12. Among the instructions, memory 220 includes a hapticeffects module 222 which are instructions that, when executed byprocessor 212, generate drive signals for haptic output device 218 thatprovide haptic effects corresponding to an event ID, as discussed aboveand disclosed in further detail below. Memory 220 may also be locatedinternal to processor 212, or any combination of internal and externalmemory.

FIG. 10 is a block diagram of a haptically-enabled system 1010 inaccordance with one embodiment.

Elements in the haptically-enabled system 1010 that are similar toelements in the haptically-enabled system 210 shown in FIG. 2 have beenreferenced using like reference numerals. With the exception of thefeatures discussed below, descriptions of the similar elements areomitted for the sake of brevity.

Referring to FIG. 10, a separate drive circuit 1016 may be separate fromthe handheld or wearable device 1030.

Alternatively, as shown in FIG. 2, the separate drive circuit 216 may bea component of the handheld or wearable device 30.

Although the specific embodiments discussed herein may include exampleevents provided in order to supply example contexts of use, one of skillwill understand that the techniques described in relation to eachembodiment can be adapted for use in other applications including eventssuch as live events, prerecorded events, plays, performances (such asballet), remotely attended events, locally attended events (for example,with wearable haptic output devices), movies at an immersive theatre,and so forth.

FIG. 3 is a flow diagram for providing event IDs in accordance with oneembodiment. In one embodiment, the functionality of the flow diagram ofFIG. 3 (and FIGS. 4-5 below) is implemented by software stored in memoryor other computer readable or tangible medium, and executed by aprocessor. In other embodiments, the functionality may be performed byhardware (e.g., through the use of an application specific integratedcircuit (“ASIC”), a programmable gate array (“PGA”), a fieldprogrammable gate array (“FPGA”), etc.), or any combination of hardwareand software.

At 310, sensor information for tracked entities can be received. Sensorinformation can include information identifying the sensor or entityassociated with the sensor and the sensor output, such as physical orenvironmental characteristics. At 320, sensor information can be used todetermine an event based on the sensor information. For example, in afootball game, sensor information from a particular sensor could becharacterized to determine that the entity associated with the sensortackled a football carrier. In another example, in a tennis match, asmart racquet can contain various sensors and connectivity to be able todetect and classify a hit. The smart racquet can detect whether thetennis ball hits on a player's racquet's “sweet spot” and associate thatkind of hit with an event ID.

Turning next to FIG. 8, FIG. 8 is a flow diagram for determining anevent based on sensor information (e.g., as diagram element 320) inaccordance with one embodiment. Sensor information can include multiplesensors assigned to multiple entities. Some of the entities can bestationary, such as a barrier sensor on a field line or goal. Some ofthe entities can be in motion, such as players or equipment in motion.At 805, a first reading of sensors can be taken. The data from sensorscan be gathered and stored. In some embodiments, sensor data can bepolled from sensors. In some embodiments, sensors can actively sendsensor data at intervals, e.g., at a sample rate. Stored sensor data canbe stored indefinitely, e.g., in non-volatile memory, or can be storedtemporarily, e.g., in a cache memory.

At 810, sensor data can be examined. In particular, some sensors mayhave instantaneous sensor information (i.e., sensor samplings) thatprovide a meaningful instantaneous value. For example, an accelerometercan provide an instantaneous value that is a value of its acceleration.Other sensors may require monitoring over time to see how their valueschange. For example, position sensor information may show a change inlocation over time from which movement can be deduced. Sensors canprovide information useful in both contexts as well. For example, anaccelerometer's readings can provide useful information in analyzing thechanges from one sample to another. At 815, for sensors providing usefulinstantaneous values, these values can be compared to a threshold valuefor that sensor. For example, an accelerometer sensor on a ball canprovide an instantaneous value indicating the ball is accelerating at avalue in an azimuth angle (such as an X or Y angle to the position ofthe accelerometer sensor). If this value is above a threshold value,then that could indicate event information for which a haptic responsemay be desired. If the value is less than the threshold value, then ahaptic response may not be warranted (even if some event could possiblybe associated with the value). Thus, if the value meets the thresholdvalue, the flow will continue to 840, otherwise the flow will continueto 835.

In addition to instantaneous values, sensors can provide values that canbe compared from one sensor sample to the next. For example, a positionsensor could be compared from one sample to the next to determinevelocity. At 820, a second sampling of the sensors can be taken. Thedata from sensors can be gathered and stored. In some embodiments,sensor data can be polled from sensors. In some embodiments, sensors canactively send sensor data at periodic sampling intervals. Stored sensordata can be stored indefinitely, e.g., in non-volatile memory, or can bestored temporarily, e.g., in a cache memory. At 825, the data from thefirst and second readings can be compared. In other words, data providedby a sensor in one sample can be compared to the data provided by thesame sensor in another sample. One of skill will understand that asecond sample of the sensors may not necessarily be the next availablesample for purposes of comparison. Also, the flow elements of 805, 810,and 815 can be repeated as needed to provide meaningful comparisons overtime. For example, a temperature sensor may not change quickly but achange from one temperature to another temperature in an hour may meet athreshold value (or condition).

At 830, the comparison values between sensor data samples are comparedto a threshold value. For example, the difference in the samples can becompared to a threshold value. In some embodiments, sensor comparisonsmay not be a direct difference, but some other statistical measure thatis compared to a threshold value. The threshold value (or condition) canalso account for a measure of time. For example, a threshold value (orcondition) may be met if a temperature sensor rises 5 degrees within anhour. In another example, a threshold value (or condition) may be met ifthe standard deviation of sample values varies by more than 10% over any50 consecutive samples.

If the threshold value is not met then flow continues to 835. At 835,the sensor information is ignored for event determination. Of course, itmay be useful to keep the sensor information for future consideration.

If the threshold value is met, then the flow continues to 840. At 840,event information will be determined from the sensor data. Two exampletracks are shown for how event information can be determined. Otheralgorithms may be used to determine event information from the sensordata. The first example algorithm consistent with some embodiments canuse a rules-based approach to examine sensor data to see if the datafits a certain rule. In some embodiments, rather than “rules,”“patterns” can be used, such as would be used in a pattern matching orpredictive-analysis-type algorithm At 845, rule data is retrieved. Ruledata can be specific to the type of live event taking place (e.g.,football game, tennis game, presidential debate, etc.). In someembodiments, a core set of rules can be developed that can begeneralized to any live event. In some embodiments, rule data can bespecific to or customized based on the participants (e.g., teams),location (e.g., stadium), time (e.g., night), date or season (e.g.,winter), a sensor information input (e.g., temperature), and so forth.Rule data can specify value ranges for sensor data for particular sensortypes and tie value ranges for one or more sensors to a particularevent. For example, one rule can be that if sensor x=1, sensor y=5, andsensor z=10, then set event ids 4, 5, and 6. In this example, sensor xmight be associated with a ball, sensor y with a first player, andsensor z with a second player; event ids 4, 5, 6, and 7, mightcorrespond to first player kickoff, team 1 kick, second player receive,team 2 receive, respectively.

In embodiments where pattern matching is used, patterns can be used bymodeling known actions. For example, sensors can be placed on entities,such as players and equipment, and data from the sensors be taken forknown events and associated with such events, thereby developing apattern profile for different events. Pattern profiles can be matched tofuture sensor data to determine one or events for a detected pattern inthe future sensor data.

At 850, sensor information can be analyzed. Whereas sensor informationthat will not match to any rules or patterns can be disregarded, sensorinformation that serves as a foundational basis for several rules can behighlighted or preprocessed for easier use. For example, anaccelerometer reading can be preprocessed to be downsampled, upsampled,or converted into an absolute value. In some embodiments, a sensor valuecan be preprocessed for finding a condition of interest which cantrigger analyzing other sensor values. For example, if an accelerationvalue is greater than a first threshold value, a hit can be detected inpreprocessing and gyroscope and other acceleration values can be storedfor further rules analysis or pattern analysis. At 855, sensorinformation can be compared to the rules criteria.

At 860, the rules can be associated with events. For example, thefollowing table tracks rules to events.

TABLE 1 Rule ID Event 1 Team 1 score 2 Team 2 score 3 Player A score 4Player B score . . . N-1 Tackle of Player A N   Tackle of Player BRule criteria can meet multiple events. For example, in Table 1, if RuleID 3 is met, then one of Rule ID 1 or 2 will also be met (assuming ateam sport with two teams).

Another example algorithm for determining event information from sensordata consistent with some embodiments can use a characterization-basedapproach to examine sensor data to characterize it into an event. At865, sensor information can be analyzed. In some embodiments, this caninclude preprocessing the data, such as described above in connectionwith 850. At 870, the sensor information can be characterized based onthe sensory inputs. Sensory inputs can be characterized into eventinformation where the event can be directly represented by a hapticeffect rather than a live event. In other words, a paradigm can be usedto evaluate sensory inputs into haptic signal events rather thanon-the-field events or at-the-venue events. The idea here is toapproximate a direct translation of input sensory signals into a subsetof event data for haptic output. The instantaneous sensor information orcomparison sensor information can be classified into different ranges,each range corresponding to a particular haptic response. For example,an accelerometer sensor on a ball can have a range defined forinstantaneous sensor values of 5-10, 10-15, 15-20, and so on. If anaccelerometer outputs a value 12, then it would fall into the secondrange. At 875, the characterization can be associated with an event.Continuing the example, the second range can have a haptic eventassociated with it that would provide a certain haptic effect. Thus, thegranularity of the sensor data can be reduced into events, which in turncan be associated with event IDs, as further described below.

One example of the flow of FIG. 8 can include sensing an event relatingto a tennis racquet. A sensor such as sensor 20 can be an IMU containingan accelerometer, gyroscope, and magnetometer. The acceleration andgyroscope measurement readings can be analyzed to determine an eventinvolving the tennis racquet. The sensor readings can be used toclassify the tennis racquet hitting a ball into events such as backhandshot, forehand shot, sweetspot hit (center of the racquet), and/or edgeshit (edges of the racquet). For example, a hit could be classified intoa forehand shot on the sweetspot of the racquet. Event IDs can beassigned to these events as described below, and eventually a hapticeffect can be played on a haptic output device of a user's device.

Classifying a hit can start with hit detection, such as in 865.Preprocessing, such as described above, can convert acceleration valuesin the y-direction into absolute values, since only the absolute valuesneed be considered. If y-acceleration is greater than a first thresholdvalue, a hit is detected, such as in 870. Gyroscope and accelerationvalues can be stored. Further preprocessing can sample the data of thez-acceleration values to determine a minimum value reached immediatelyfollowing the detection of the y-acceleration exceeding the firstthreshold value, such as in 850. Establishing a minimum value will allowa comparison of values within a minimum to maximum range of values todifferent threshold values to characterize the event. Sensor values willvary depending on the nature of the hit.

FIG. 9 shows an example output of z-acceleration values that can besampled following a hit. Time period 905 precedes the hit. At time 910,an instantaneous value shows a high peak maximum value. In they-acceleration direction a similar response value can exceed a thresholdvalue and trigger further sampling of sensor data. At time period 915,sensor values for z-acceleration are slightly attenuating. At timeperiod 920, the racquet is nearly back to normal, with z-accelerationvalues being nearly zero. At time period 925, the racquet is no longervibrating as a result of the hit. The time period from 905 to 925 cantake place within 100 ms.

The event can be classified using the available values of the sensor,including those discussed above and values from the gyroscope axes. Forexample, the events as described above can be sensed by implementing aprocedure, such as in 855 and 860, as outlined by the followingpseudocode:

If (hitOccured (i.e., y-acceleration > threshold value 1))   If(Gyroscope x value at hit < threshold value 2)     Backhand hit   ElseIf (Gyroscope x value at hit > threshold value 3)     Forehand hit   If(z-accelerationMinimum value > threshold value 4)     Sweetspot hit  Else If (z-accelerationMinimum value < threshold value 5)     Edgespots hit.

As found in the pseudocode, a z-acceleration minimum value found frompreprocessing allows the detection of sweetspot versus edge location ofa hit, as each data pattern that occurs beginning immediately after thehit is different. The differences can be modeled and using patterns fromthe modeling, the values for thresholds 4 and 5 can be determined todeduce the hit location. Likewise, values for thresholds 2 and 3relating to the gyroscope x axis value can be found using modeling todetermine whether the hit is a forehand or backhand hit.

Hit intensity can also affect the haptic effect that is eventuallyoutput on a haptic output device. To find whether the hit was “soft,”“medium,” or “hard” a function of one or more of the accelerationparameters on the axes of the sensor can be compared to a soft thresholdvalue and a hard threshold value. If the parameters indicate the hit wasbelow the soft threshold value, the intensity of the hit can beclassified as soft. If the parameters indicate the hit was above thehard threshold value, the intensity of the hit can be classified ashard. Otherwise the intensity of the hit can be classified as medium.

The following pseudocode illustrates one way of assigning an hit IDusing the hit intensity as another parameter of the characterization ofthe event.

If (SoftHit and SweetSpot)   hitID = 1 else if (SoftHit and Edges)  hitID = 2 else if (hardHit and SweetSpot)   hitID = 3 else if (hardHitand SweetSpot)   hitID = 4 else   hitID = 0The hit ID can be translated into an event ID. Forehand or Backhand canalso be added to create other hitIDs.

One of skill in the art will understand that different sensors anddifferent applications would call for different events and eventmodeling than laid out above for a tennis racquet hit. The nature of theactivity will dictate what sensor values are evaluated and whatthreshold values are found to model or pattern match event activities.In some embodiments, a human operator can choose which sensors or datastreams associated with which sensors should be used given the nature ortype of activity being evaluated. In some embodiments, artificialintelligence techniques can be used by observing all data and choosingdata of interest given some criteria, such as a level of informationentropy.

As described above, preprocessing can be used to highlight or adjustdata prior to evaluation against a threshold value. Preprocessing canalso be useful when more data points (or sensor parameters) are neededto determine an event ID for a detected event, such as a hit. Forexample, as available data bandwidth of sensor information is lower,additional preprocessing can consider additional parameters to analyzesensor information. In another example, a sensor could be providingscarce data. Preprocessing can analyze other parameters to examine therelationship between different sensor's values. For example, a currentvalue for acceleration in the y direction can be compared to a pastvalue for acceleration in the y direction. The same can be done foracceleration in the z direction. If the z axis is more prominent, the zacceleration value can be substituted in the preprocessing step todetermine a hit. The relationship between current sensor data andprevious sensor data can indicate which axis is most affected by a hit.If scarce data prevents from detecting the hit on one axis, it could bedetected on another axis using its acceleration values. Sensor datamight be scarce in the expected direction because the sensor may bemalfunctioning, misaligned, or the equipment may be used in anunexpected fashion.

Turning back to FIG. 3, at 330, an event ID can be assigned to the eventthat was determined from the sensor information. The event ID could bean ID that indicates a particular event associated with a specific typeof live event, like a football game. In some embodiments, the event IDcan indicate a generic type of desired haptic feedback, such as an eventID that corresponds to activating a vibrotactile actuator at a 75%intensity level for 500 ms. The event ID can result from atransformation of N input sensory signals, such as acceleration (x, y,z), gyroscope (x, y, z), magnetometer (x, y, z), etc., into M outputs toclassify an event ID.

At 340, the event ID is sent to a haptically-enabled device of a user.As described below, the event ID may be accompanied by other processingbefore it reaches the user.

FIG. 4 is a flow diagram of providing event IDs in accordance with oneembodiment. At 402, sensor information can be grouped by classificationor source. A classification or source can correspond to the entity ortype of entity from which sensor information is received. For example,the classification or source of an entity can be a player, team, person,object, ball, goal, boundary, referee, and so forth. Multiple sensorscan also be associated with one entity. For example, multiple sensorscan be placed on a player and all associated with that one player. At404, sensor information can be merged into a multi-channel feed. Asensor interface such as sensor interface 21 can receive all the sensorinformation and group it. The sensor interface can be in proximity tothe sensors (i.e., local to the event) and merge the sensor informationinto a multi-channel feed. In some embodiments, the sensor interface canbe located remotely (i.e., remote from the event). At 406, themulti-channel feed can be provided as merged sensor information to anevent process server.

At 410, sensor information for tracked entities can be received. Sensorinformation can include identity information for identifying the sensoror entity associated with the sensor and can include the sensor output,such as physical or environmental characteristics as measured by thesensor. At 420, sensor information can be used to determine an eventbased on the sensor information. The flow of FIG. 8 can be used todetermine an event, as described above. For example, in a football game,sensor information from a particular sensor can be characterized todetermine that an entity associated with the sensor is a player tacklinga football carrier. At 430, an event ID can be assigned to the eventthat was determined from the sensor information. The event ID can be anID that indicates a particular event associated with a specific type oflive event, like a football game. In some embodiments, the event ID canindicate a generic type of desired haptic feedback, such as an event IDthat corresponds to activating a vibrotactile actuator at a 75%intensity level for 500 ms.

At 432, one or more optional classification or source IDs can beassigned to the event. For example, a player dunking a basketball can beassigned a classification or source ID for the identification of theplayer and a classification or source ID for the team the player is on.“Classification” or “source” can be substituted for the classificationor source of the thing. For example, the classification or source IDcould be “player ID,” “team ID,” “goal ID,” “hole ID,” “object ID,”“boundary ID,” and so forth. At 434, one or more classification orsource IDs can be associated with the event ID. Classification or sourceinformation can include optional force information. Force informationcan be an absolute parameter indicating an intensity that the hapticeffect should be played (e.g., low, medium, hard), or it can be avariance parameter indicating an intensity that the haptic effect shouldbe varied from a baseline intensity for the haptic effect (e.g., lower,harder, much harder). The event ID can be based on a transformation of Ninput sensory signals, such as acceleration (x, y, z), gyroscope (x, y,z), magnetometer (x, y, z), etc., into M outputs to classify an eventID, force, player, team, and so forth.

At 436, event IDs (and optional classification or source IDs) can bemerged into a multichannel feed. At 438, optional delay can be usedprior to sending the feed, such as discussed above with regard to FIG.7. At 440, event IDs (and optional source or classification IDs) can besent to a user. In some embodiments, the feed can be broadcast withaudio and video for a live event. In other embodiments, the feed can betransmitted using other wired or wireless technology.

FIG. 5 is a flow diagram of providing haptic output at a haptic outputdevice in accordance with one embodiment. At 510, output preferences fora user can be received. In some embodiments, output preferences caninclude preferred event IDs. In some embodiments, output preferences caninclude preferred classifications or sources for filtering event IDs bya corresponding classification or source ID. At 520, event IDs can bereceived. In some embodiments, event IDs are received at process server28, which can be a standalone machine, part of system 210, or part of areceiving device, such as a network device. At 530, a haptic effect isselected based on one or more event IDs. In some embodiments, outputpreferences can provide filtering so that only certain event IDs arereceived at processor 212. In other embodiments, event IDs and optionalclassification or source IDs can be received at processor 212 forfiltering. In some embodiments, output preferences can allow the user toenable or disable different haptic channels provided by event processserver 22, where each haptic channel corresponds to an event ID.Processor 212 can use module 222 to determine which haptic effect tooutput based on the event ID. Module 222 can include a lookup table thatassociates the event ID and optional classification or source IDs (suchas a team ID or player ID) with a particular haptic effect, as in thefollowing table:

TABLE 2 Event ID Haptic Effect 1 Effect 1 (force, team, player) 2 Effect2 (force, team, player) . . . N Effect N (force, team, player)

In some embodiments, the force element can be a separate parameter,specified as metadata to the event ID, or in some embodiments, the eventID can specify a separate force parameter, such as in Table 3.

TABLE 3 Event ID Haptic Effect Force of the effect 1 Effect 1 (team,player) Force 2 Effect 2 (team, player) Force . . . N Effect N (team,player) Force

Team and Player can serve as filters. If a user receives event IDs 1 and2 at the same time and ID 1 has team specified as Team A and ID 2 hasteam specified as Team B, and the output preferences enable Team A anddisable Team B, only the effect for ID 1 will be output (Effect 1).

At 540, the selected haptic effect is outputted on the haptic outputdevice 218. At 550, a user can optionally update output preferences. Forexample, output preferences can be updated to focus on a differentclassification or source by changing event ID filtering to adjust basedon the updated output preferences. Updating output preferences can causea different event ID to be selected, even if the event giving rise tothe event ID is substantially similar to another event where one eventID was selected. For example, for a touchdown score in a football game,haptic feedback for the scoring team may be designed to be “cheerful”feeling, high-frequency vibrotactile feedback, whereas haptic feedbackfor the non-scoring team may be designed to be “negative” feeling,low-frequency vibrotactile feedback. Two Event IDs can be sent, one foreach type of feedback, each specifying classification or source IDscorresponding to the two respective teams. The output preferences forone team would provide feedback corresponding to one event ID whereasoutput preferences specifying the other team would provide feedbackcorresponding to a different event ID. The resulting haptic feedbackwould be different for each event ID even though the event generatingthe event IDs is the same, i.e., the touchdown.

In one example embodiment of system 10, a user can watch a Los AngelesLakers basketball game on a device 30 such as a tablet. Suppose the useris an avid Lakers fan. He enables haptic effects for the live game andspecifies preferences to receive haptic feedback based on the Lakersmoves. As the Lakers play, entity sensors 20 provide sensor informationfrom sensor IDs associated with the different entities on the court,such as goals, players, boundaries, officials, the ball, and so forth.System 10 can receive the sensor information 20 and determine an eventbased on the sensor information. Such events may include ball relatedevents, such as dribbling, shooting the ball, scoring, and so forth, orplayer related events, such as blocking, running, jumping, and so forth.Each of the events can be classified by the source of the entity orentities associated with the event, so that all the events associatedwith the Lakers are classified as such. An event ID and source ID can beassigned to the events. All of the event IDs and source IDs can be sentto a haptically-enabled device, such as the user's tablet.

The user's tablet can receive the event IDs and filter them so that onlythe event IDs that also have a source ID for the Lakers are used. Ahaptic effect can be selected for each of the event IDs that correspondto the Lakers. The haptic effect can be played on the tablet. This canall happen so that the event IDs are processed at the same time as thevideo feed to align the haptic effect with the video on the screen.

After half-time, the user can decide to focus more on a particularplayer, such as Kobe Bryant. The user enables haptic effects just forthat player. System 10 can respond by filtering on source IDscorresponding to that player. Now the user will only receive hapticfeedback based on what that player is doing.

In another example, another user watches a live tennis game. The useractivates haptic effects for the game. The user can distinctively feelracquet hit effects of the players. She can feel the intensity and typeof hit, making her more excited and immersed in the game. She laterdecides to enable haptic effects only for serving shots. She can feelthe difference in the intensity of haptic effects between the player'sfirst service attempt and second service attempt (where the playertypically serves with less force in favor of better accuracy).

In another example, another user is streaming a live soccer game on hisphone. The user is unable to view the screen because he is multitasking.The user puts the phone on his lap and enables haptic effects for whenthe ball is within striking distance of the goal and in the control ofthe potentially scoring team. He can then focus on his other work andnot miss a goal in the game.

In another example, another user is streaming a live soccer game. Theuser can choose the type of haptic effect preferred when players shootthe ball using their feet or their heads. Customizing which hapticeffects are played allows the user to have a unique experience.

As disclosed, embodiments provide a system for generating haptic effectsfor events based on an event ID. Sensors can gather information, send itto an event process server to determine an event ID, and event IDs canbe provided to handheld or wearable devices for output on haptic outputdevices contained thereon. An event ID can correspond to a particularhaptic effect so that haptic logic need not be provided directly fromthe event venue to the audience venue. Some embodiments specify aclassification or source ID associated with the event ID that allowsusers to select haptic output to be provided based on characteristics ofthe source of the sensor information giving rise to the haptic effect.

Several embodiments are specifically illustrated and/or describedherein. However, it will be appreciated that modifications andvariations of the disclosed embodiments are covered by the aboveteachings and within the purview of the appended claims withoutdeparting from the spirit and intended scope of the invention.

1. A method of generating event identifiers (IDs), comprising: receivingsensor information that corresponds to signals generated by sensorsrespectively on tracked entities; determining an event based on thesensor information; assigning an event ID corresponding to the event;and sending the event ID to a haptically-enabled device for output of ahaptic effect determined from the event ID.
 2. The method of claim 1,further comprising: classifying the event based on an association of oneor more of the tracked entities with the sensor information; assigningat least one classification ID to the event, wherein the at least oneclassification ID specifies a particular person or object associatedwith a respective one of the one or more tracked entities; andassociating the at least one classification ID with the event ID.
 3. Themethod of claim 2, wherein the sending further includes sending the atleast one classification ID with the event ID for output of the hapticeffect determined from the event ID and the at least one classificationID.
 4. The method of claim 3, wherein the haptic effect is furtherdetermined from user preferences that specify a classificationpreference, and the method further comprises: when matching the at leastone classification ID to the classification preference, and selectingthe event ID based on the matching the at least one classification ID tothe classification preference.
 5. The method of claim 1, furthercomprising: merging the event ID with other event IDs into amultichannel feed.
 6. The method of claim 5, wherein the sending furtherincludes transmitting the multichannel feed to a second process server,and the second process server is configured to process the multichannelfeed and provide one or more of the event ID and the other event IDsfrom the multichannel feed to the haptically-enabled device.
 7. Themethod of claim 2, further comprising: prior to the receiving sensorinformation, grouping the sensor information by source; merging thesensor information into a multi-channel feed; and providing themulti-channel feed as the sensor information for the tracked entities.8. The method of claim 1, further comprising: prior to the sending theevent ID, delaying the sending by a time based on an intentional delayin a distribution of an associated audio/video signal and an event IDprocessing time.
 9. A system for generating event identifiers (IDs),comprising: a sensor information interface configured to receive sensorinformation that corresponds to signals generated by sensorsrespectively on tracked entities; an event processing server configuredto determine an event based on the sensor information, assign an eventID corresponding to the event, and send the event ID to ahaptically-enabled device for output of a haptic effect determined fromthe event ID.
 10. The system of claim 9, wherein the event processingserver is further configured to: classify the event based on anassociation of one or more of the tracked entities with the sensorinformation, assign at least one classification ID to the event, whereinthe classification ID specifies a particular person or object associatedwith a respective one of the one or more tracked entities, and associatethe at least one classification ID with the event ID.
 11. The system ofclaim 10, wherein the event processing server is further configured tosend the at least one classification ID with the event ID for output ofthe haptic effect determined from the event ID and the at least oneclassification ID.
 12. The system of claim 11, further comprising: asecond process server configured to determine the haptic effect from theevent ID and from user preferences specifying a classificationpreference, wherein the second process server matches the at least oneclassification ID to the classification preference, and selects theevent ID based on the at least one classification ID matched to theclassification preference.
 13. The system of claim 9, furthercomprising: an event ID merger configured to merge the event ID withother event IDs into a multichannel feed.
 14. The system of claim 13,further comprising: a second process server configured to receive themultichannel feed, process the multichannel feed, and provide one ormore event IDs to the haptically-enabled device.
 15. The system of claim10, wherein: the sensor information interface is further configured togroup the sensor information by source, merge the sensor informationinto a multi-channel feed, and provide the multi-channel feed as thesensor information for the tracked entities.
 16. The system of claim 9,wherein the event processing server is further configured to: prior tothe event ID being sent to the haptically-enabled device, delay theevent ID from being sent by a time based on an intentional delay in adistribution of an associated audio/video signal and an event IDprocessing time.
 17. A method of generating a haptic effect from anevent identifier (ID), comprising: receiving a selection of outputpreferences from a user; receiving an event ID, the event ID beingdetermined based on an event, the event being determined based on sensorinformation that corresponds to signals generated by sensorsrespectively on tracked entities; selecting a haptic effect based on theevent ID and the output preferences; and outputting the haptic effect ona haptic output device.
 18. The method of claim 17, further comprising:receiving with the event ID, a classification ID based on an associationof one or more of the tracked entities with the sensors.
 19. The methodof claim 18, wherein the output preferences specify a classification,and when the classification ID matches the classification, the selectinga haptic effect is based on the classification ID matched to theclassification.
 20. The method of claim 17, wherein: the event ID isselected from a multichannel feed with other event IDs, the selectingbeing based on the output preferences.
 21. The method of claim 20,further comprising: updating the output preferences so as to cause adifferent event ID to be selected from the multichannel feed.
 22. Asystem of generating a haptic effect from an event identifier (ID),comprising: a device configured to: receive a selection of outputpreferences from a user, receive an event ID, wherein the event ID isdetermined based on an event, wherein the event is determined based onsensor information that corresponds to signals generated by sensorsrespectively on tracked entities, select a haptic effect based on theevent ID and the output preferences, and output the haptic effect on ahaptic output device.
 23. The system of claim 22, wherein the device isfurther configured to: receive with the event ID, a classification IDbased on an association of one or more of the tracked entities with thesensors.
 24. The system of claim 23, wherein the output preferencesspecify a classification, and when the classification matches theclassification ID, the device is configured to select the haptic effectbased on the the classification matched to the classification ID. 25.The system of claim 22, wherein: the event ID is selected from amultichannel feed with other event IDs, the selecting being based on theoutput preferences.
 26. The system of claim 25, further comprising:updating the output preferences so as to cause a different event ID tobe selected from the multichannel feed.